Paper Authors

Kurt Rosentrater
USDA-ARS

KURT A ROSENTRATER is a Lead Scientist with the United States Department of Agriculture, Agriculture Research Service, in Brookings, SD, where he is spearheading a new initiative to develop value-added uses for residue streams resulting from biofuel manufacturing operations. He is formerly an assistant professor at Northern Illinois University, DeKalb, IL, in the Department of Technology. He received the Faculty of the Year award in 2002 sponsored by the NIU College of Engineering and Engineering Technology.

Jerry Visser
South Dakota State University

JERRY VISSER is Operations Manager of the Product Development Center at South Dakota State University in Brookings, SD, where he brings conceptual ideas to tangible products. He serves as a faculty member for the Manufacturing Engineering Technology Program. He leads the American Society for Quality as Chair of the Southeast South Dakota Sub-section.

Abstract

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Simulation as a Means to Infuse Manufacturing Education with
Statistics and DOE – A Case Study using Injection Molding

Abstract

Modern manufacturing systems continue to evolve and in so doing can produce many unique
products using both traditional as well as novel raw materials. This is especially true in the
processing of plastic products. In these environments, there is the need to critically examine
material compatibility and to optimize methods of manufacture to realize economic success.
Key to these endeavors is the ability to conduct product development efforts in a logical fashion.
Experimentation is an important component to this process. Graduates of manufacturing
engineering and technology programs should thus have knowledge of formal Design of
Experiments (DOE) and statistical procedures. But, most undergraduate students are not
exposed to these methodologies – only in graduate level statistics classes do engineering and
technology students typically receive this type of training. Moreover, implementing formal,
hands-on experiments can be problematic in many undergraduate curricula because they can be
extremely time and resource consuming. Computer simulation can be one way to effectively
implement and achieve these objectives, though. The goal of this paper is to describe how to use
simulation software to conduct formal experiments using dedicated injection molding software.
This paper will discuss several key topics, including a brief introduction regarding the teaching
of statistics and DOE to engineering and technology students, as well as injection molding, a
common manufacturing unit operation. An example simulation exercise will then be presented
to illustrate the concepts discussed. Educators in manufacturing programs should find this
useful as they consider how best to augment laboratory work, student understanding of statistics,
as well as to achieve proficiency with computer simulation, as this approach to laboratory
experiences transcends injection molding specifically, and has a wide range of applicability with
many manufacturing operations.

Introduction

As evidenced by the many presentations at annual ASEE national and regional meetings,
educators are constantly developing and implementing improved curricula to meet emerging
challenges in the various fields of engineering and technology. Some of these activities
encompass developing novel subject matter. Many of these endeavors, however, include
teaching fundamental, traditional topics using new methods, approaches, and strategies.

Statistics is a skill that is essential for all engineering and technology professionals, but has not
been overly emphasized over the years. Many graduates will frequently need to use these tools
once they enter the workforce. This is especially true for those involved in research and
development as well as testing and validation activities. Basic and applied statistics is key to
analyzing laboratory studies, deciphering what the data mean, and discerning trends and
patterns1. Even so, the teaching of statistics to engineers has been the subject of only a few
studies in recent years2-4. Essential statistics topics should include independent and dependent
variables, factorial experimental designs and coding schemes, summarizing collected data with
estimates of central tendency (i.e., means) and estimates of observed error (i.e., standard